UCF-Crime-DVS A Novel Event-Based Dataset for Video Anomaly Detection with Spiking Neural Networks

Chong Wang

Abstract:

Video anomaly detection plays a significant role in intelligent
surveillance systems. To enhance model’s anomaly recogni-
tion ability, previous works have typically involved RGB, op-
tical flow, and text features. Recently, dynamic vision sensors
(DVS) have emerged as a promising technology, which cap-
ture visual information as discrete events with a very high
dynamic range and temporal resolution. It reduces data re-
dundancy and enhances the capture capacity of moving ob-
jects compared to conventional camera. To introduce this rich
dynamic information into the surveillance field, we created
the first DVS video anomaly detection benchmark, namely
UCF-Crime-DVS. To fully utilize this new data modality, a
multi-scale spiking fusion network (MSF) is designed based
on spiking neural networks (SNNs). This work explores the
potential application of dynamic information from event data
in video anomaly detection. Our experiments demonstrate the
effectiveness of our framework on UCF-Crime-DVS and its
superior performance compared to other models, establish-
ing a new baseline for SNN-based weakly supervised video
anomaly detection.